If data silos were Lex Luthor then data warehousing would be Superman.
When your business comes across valuable information it needs to be centralized and accessible to your entire organization. Data warehousing creates a system where these resources are automatically processed and shared with the appropriate parties. By organizing data into one location, your employees can solve problems faster and consistently meet deadlines.
Since data warehousing is often confused with other similar concepts, let's start with defining what it is and identifying a few different types of data warehouses.
Data Warehousing Concepts: What Is Data Warehousing?
Data warehousing is a technology that aggregates and analyzes data from a variety of sources. These resources reveal meaningful insights about a business and its customer base. Using this repository of information, company leaders can justify major decisions by backing their ideas up with qualitative and quantitative data.
With a data warehouse, information flows in consistently while analysts review it. This then makes it possible for businesses to create reports and dashboards that continuously monitor and improve business functions.
One note to keep in mind is that data warehouses differ from databases. While both features have a similar objective, the functionality of each one differs significantly.
Data Warehouse vs. Database
Databases are structures that organize data into rows and columns making the information easier to read. Data warehouses are databases that go a step forward by allowing for data analysis. They don't just collect and organize data but they also aggregate it for long-term business use.
Additionally, data warehouses aren't the same as "data lakes." While a data lake stores information, its content is different than what a data warehouse contains.
Data Warehouse vs. Data Lake
While both data lakes and data warehouses store information, the type of data differs greatly. Data lakes store raw data that has yet to be filtered and defined. Data warehouses store structured data that has already been filtered and defined for a specific use.
Because data lakes include raw data, the data is simpler to use, more readily accessible, and easier to edit. In data warehouses, the data isn't as accessible and is more expensive to make changes to, yet it's better to use for long-term decisions.
Types of Data Warehouses
Enterprise Data Warehouse
Operational Data Store
1. Enterprise Data Warehouse
Enterprise data warehouses are central databases where data is organized, classified, and used for decision-making. These systems will also label data and categorize for easier access.
2. Operational Data Store
While enterprise data warehouse is better for long-term business decisions, an operational data store (ODS) is preferred for daily, routine activities. ODS is updated in real-time and stores data specific to a chosen activity.
3. Data Mart
A data mart is a part of a data warehouse. It's designed to support a specific department, team or function. Any information that passes through is automatically stored and organized for later use.
Now that you're familiar with the fundamentals of data warehouses, let's take a look at some common concepts used by most businesses.
3 Data Warehouse Concepts with Examples
1. Basic Data Warehouse
A basic data warehouse aims to minimize the total amount of data that's stored within the system. It does this by removing any redundancy within the information, making it clear and easy to look through.
As you can see in the example below, this concept centralizes information from a variety of sources. Employees then access data directly from the warehouse. This system is useful for SMB who want a simple approach to data storage.
Some data warehouses clean and process data before moving it into storage. These systems have "staging areas" where information is reviewed, evaluated, then deleted or transferred into the warehouse. This ensures that only relevant and useful data is stored within the software.
If you look at the example below, you can see that the staging area is placed between the data sources and the warehouse. For businesses that process large amounts of customer data, this process will filter out irrelevant information that isn't beneficial to your team.
Data marts add another level of customization to your data warehouse. Once data is processed and evaluated, data marts streamline information to teams and employees who need it most. That makes your departments significantly more productive because customer data is being delivered directly to them.
In the example below, we can see how data marts are used to send information to Sales and Inventory teams. This helps business leaders make faster decisions and capitalize on timely marketing opportunities.